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Real Applications of Quantum Imaging

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Real Applications of Quantum Imaging Real applications of quantum imaging Marco Genovese INRIM, strada delle Cacce 91, 10135 Turin, Italy and CNISM, sezione di Torino Universit`a,via P.Giuria 1, 10125 Turin, Italy E-mail: [email protected] Abstract. In the last years the possibility of creating and manipulating quantum states of light has paved the way to the development of new technologies exploiting peculiar properties of quantum states, as quantum information, quantum metrology & sensing, quantum imaging ... In particular Quantum Imaging addresses the possibility of overcoming limits of classical optics by using quantum resources as entanglement or sub-poissonian statistics. Albeit quantum imaging is a more recent field than other quantum technologies, e.g. quantum information, it is now substantially mature for application. Several different protocols have been proposed, some of them only theoretically, others with an experimental implementation and a few of them pointing to a clear application. Here we present a few of the most mature protocols ranging from ghost imaging to sub shot noise imaging and sub Rayleigh imaging. PACS numbers: 42.50.-p,42.30.-d,42.50.St,03.65.-w 1. Introduction In the last decade the possibility of manipulating single quantum states, and in particular photons, fostered the realization of technologies exploiting the peculiar properties of these systems (and in particular quantum correlations [1]), collectively dubbed quantum arXiv:1601.06066v1 [quant-ph] 22 Jan 2016 technologies [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. Among quantum technologies, quantum imaging [15, 16] addresses the possibility of beating the limits of classical imaging by exploiting the peculiar properties of quantum optical states [17, 18, 19, 20, 21]. In this topical review we would like to present, without any pretension to be exhaustive, some of the most interesting examples of quantum imaging protocols that can find application in a near future. We will introduce their main features, discuss the quantum resources really needed for their implementation and, finally, describe the present state of the art of their development. Section 2 will be devoted to ghost imaging. After introducing this technique, we will clarify what resources are really needed for this protocol and what it is achievable with classical resources. Then, a few practical applications will be presented. In section three, 2 we will introduce sub shot noise imaging. Here we will also discuss the resources needed for this scheme. In section four, quantum illumination will be examined, comparing the original theoretical idea with experimental realizations. In section five, we will review a few schemes for sub Rayleigh imaging based on quantum light. Finally, we will hint at a few more possible developments of quantum imaging, drawing some conclusion on the future of this quantum technology. 2. The ghost imaging Among quantum imaging techniques, ghost imaging [22] (GI) was one of the first to be proposed and attracted a large interest. This technique exploits intensity correlation fluctuations for imaging an object crossed by a beam that is revealed by a detector without any spatial resolution (bucket detector). The image of the object is retrieved when the bucket detector signal is correlated with the signal of a spatial resolving detector measuring a light beam (reference beam) whose noise (the speckles pattern [23, 24, 25]) is spatially correlated to this one. This result may seem amazing sice the image is reconstructed by the spatial information gathered by the detector that did not actually interacted with the object. Nevertheless, even at an intuitive level this result can be understood from the fact that one is correlating in several different frames the fluctuations of the speckle structure of the reference beam with the effect of absorption (reflection) pattern of the imaged object on the correlated beam speckles structure. More in detail, for realising GI, a spatially incoherent beam is addressed to an object and then collected by a bucket detector without any spatial resolution. The correlated beam, not interacting with the object, is sent to a spatially resolving detector (an array of pixels); K frames are collected. The image of the object is retrieved by measuring a function S(xj)(xj being the position of the pixel j in the reference region): 2 2 S(xj) = f(E[N1];E[N2(xj)];E[N1N2(x)];E[N1 ];E[N2 (xj)]; :::); p q where E[N1 N2 (xj)]; (p; q ≥ 0) is the correlation function of N1, the total number of photons collected at the bucket detector, and of N2(xj), the photon number at the j- th pixel of the reference arm; E[X] being the mathematical average over the set of K frames. Substantially, in most of experimental works three main functions S have been used: i) the Glauber intensity correlation function (2) S(x) = G (x) ≡ E[N1N2(x)] (1) ii) the normalized intensity CF (2) (2) S(x) = g (x) ≡ G =(E[N1]E[N2(x)]) (2) iii) the covariance, or CF of intensity fluctuations, S(x) ≡ Cov(x) = E[(N1−E[N1])(N2(x)−E[N2(x)])] = E[N1N2(x)]−E[N1]E[N2(x)](3) 3 In order to discuss the performance of different ways for realising the GI, the most significant figures of merit are the resolution and the signal-to-noise ratio (SNR) [26, 27, 28, 29] j hSin − Souti j SNRS ≡ q ; (4) 2 hδ (Sin − Sout)i where Sin and Sout are the intensity values of the reconstructed ghost image, when xj is either inside or outside the object profile, δS ≡ S − hSi is the fluctuation. The SNRS quantifies how well the image of the object is distinguishable from the background. On the other hand, the resolution is determined by the number of speckles contained in the image and quantifies the number of elementary details of the object that can be distinguished. In view of practical application, several studies addressed a clear theoretical description [27, 30, 31, 32, 33, 34, 35] and eventual improvements of this protocol [36, 37, 38, 39, 40], as the use of high order correlation functions or differential schemes. In particular, a systematic analysis, both theoretical and experimental, was presented [21], demonstrating, among other results, that the cases (ii) and (iii) perform very similarly in terms of SNRS, while the case (i) performs much worse. In the original proposal [22] and in the earliest experimental demonstrations of this technique [41] nonclassical states of light, known as twin beams, were used. These states, produced by a non-linear optical phenomenon dubbed parametric down conversion [42], PDC, (or eventually in other non-linear optical phenomena as 4-wave mixing) are correlated in photon number and are of the form jtwini = ΣnCnjnijni (5) where jni is the n photons Fock state. They present a thermal statistics at single mode level and a speckle (each speckle corresponding to a spatial mode) structure at spatial multimode level. Nevertheless, it was later shown, both theoretically and experimentally, that GI can be realised also with beam-split thermal light [43, 44, 45, 46, 47, 48, 49], eventually even sunlight [50], although with a smaller visibility. Indeed by considering that the field component obeys Z Z + 2 0 0 3 −ikr0 Ei (r; t) = d r h(r; r ) d ke a(k) (6) where a(k) is the field annihilation operator for the optical mode k and h(r; r0) the response function of the optical path, the correlation function of intensity fluctuations can be derived for the thermal case: Z Z 0 0 ∗ 0 0 y 0 2 CovTH (x) / dyj dx dy h1(x; x )h2(y; y )ha (x')a(y )ij (7) bucket area where h1 and h2 are the response functions on the path directed to the spatial resolving and the bucket detector respectively, while a(x) = R d3ke−ikxa(k). On the other hand for twin beams: Z Z 0 0 0 0 y 0 2 CovTB(x) / dyj dx dy h1(x; x )h2(y; y )ha (x')a(y )ij (8) bucket area 4 ∗ A part some numerical factor and the presence of h1 instead of h1 the two CF of intensity fluctuations are analogous. In particular [43], when T (x) is the transmission function of the object to be imaged followed by a lens at a focal distance f from 0 2πi 0 0 the object and from the detection plane, h2(y; y ) / exp(− λf y · y )T (y ). When on the other path only a lens is present at a distance 2f from both the source and the 0 0 iπ 0 2 detection plane, h1(x; x ) = δ(x + x ) exp(− λf jx j ); by assuming that smallest scale of variation of the object spatial distribution is larger than the coherence size (speckle size), since hay(x')a(−y)i is non-zero in a small region around x0 = −y, one finds Cov(x) / jT (−x)j2 for both cases. In this sense, GI does not represent a "true" quantum imaging protocol, since it does not strictly require quantum light for being realised. Anyway, it must be mentioned that split thermal light has a non-zero discord, where non-zero discord is a weaker form of quantum correlation respect to entanglement z, eventually to be seen as the resource needed for GI [51]. However, the fact that GI does not really strictly needs quantum resources is even more evident in the so-called computational ghost imaging [52] (for latest developments see [53]), where discord does not play a role [54]. In this scheme a single light beam is used by modulating its pattern. The measured correlation is between the light detected by the bucket detector and the modulation pattern.
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